Abstract In this paper we present a formulation of k-anonymity as a mathematical optimization problem. In solving this formulated problem, k-anonymity is achieved while maximizing the utility of the resulting dataset. Our formulation has the advantage of incorporating different weights for attributes in order to achieve customized utility to suit different research purposes. The resulting formulation is a Mixed Integer Linear Program (MILP), which is NP-complete in general. Recognizing the complexity of the problem, we propose two practical algorithms which can provide near-optimal utility. Our experimental evaluation confirms that our algorithms are scalable when used for datasets containing large numbers of records.

中文翻译：

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基于优化的匿名算法

摘要 在本文中，我们提出了 k-匿名性作为数学优化问题的公式。在解决这个公式化的问题时，在最大化结果数据集的效用的同时实现了 k-匿名性。我们的公式具有为属性合并不同权重的优势，以实现定制的效用以适应不同的研究目的。生成的公式是混合整数线性规划 (MILP)，通常是 NP 完全的。认识到问题的复杂性，我们提出了两种实用的算法，可以提供接近最优的效用。我们的实验评估证实，当用于包含大量记录的数据集时，我们的算法是可扩展的。